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--- |
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license: bsd-3-clause |
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base_model: Salesforce/codegen-350M-mono |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: codegen-350M-mono-measurement_pred-diamonds-seed4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# codegen-350M-mono-measurement_pred-diamonds-seed4 |
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This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3733 |
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- Accuracy: 0.9086 |
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- Accuracy Sensor 0: 0.9144 |
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- Auroc Sensor 0: 0.9506 |
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- Accuracy Sensor 1: 0.9050 |
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- Auroc Sensor 1: 0.9584 |
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- Accuracy Sensor 2: 0.9332 |
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- Auroc Sensor 2: 0.9753 |
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- Accuracy Aggregated: 0.8820 |
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- Auroc Aggregated: 0.9557 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 64 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Sensor 0 | Auroc Sensor 0 | Accuracy Sensor 1 | Auroc Sensor 1 | Accuracy Sensor 2 | Auroc Sensor 2 | Accuracy Aggregated | Auroc Aggregated | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:| |
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| 0.3029 | 0.9997 | 781 | 0.3411 | 0.8441 | 0.8553 | 0.9103 | 0.8390 | 0.9066 | 0.8633 | 0.9334 | 0.8188 | 0.8975 | |
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| 0.2003 | 1.9994 | 1562 | 0.2859 | 0.8852 | 0.8929 | 0.9380 | 0.8778 | 0.9380 | 0.9319 | 0.9638 | 0.8384 | 0.9361 | |
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| 0.1366 | 2.9990 | 2343 | 0.2701 | 0.8945 | 0.9041 | 0.9549 | 0.8902 | 0.9570 | 0.9245 | 0.9755 | 0.8591 | 0.9539 | |
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| 0.0812 | 4.0 | 3125 | 0.2992 | 0.9046 | 0.9166 | 0.9542 | 0.8947 | 0.9585 | 0.9339 | 0.9765 | 0.8730 | 0.9567 | |
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| 0.0381 | 4.9984 | 3905 | 0.3733 | 0.9086 | 0.9144 | 0.9506 | 0.9050 | 0.9584 | 0.9332 | 0.9753 | 0.8820 | 0.9557 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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